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   <div id="projectbrief">Reranking framework for structure prediction and discriminative language modeling</div>
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<a href="perceptron-model-proto-reader_8_c.html">Go to the documentation of this file.</a><div class="fragment"><pre class="fragment"><a name="l00001"></a>00001 <span class="comment">// Copyright 2012, Google Inc.</span>
<a name="l00002"></a>00002 <span class="comment">// All rights reserved.</span>
<a name="l00003"></a>00003 <span class="comment">// </span>
<a name="l00004"></a>00004 <span class="comment">// Redistribution and use in source and binary forms, with or without</span>
<a name="l00005"></a>00005 <span class="comment">// modification, are permitted provided that the following conditions are</span>
<a name="l00006"></a>00006 <span class="comment">// met:</span>
<a name="l00007"></a>00007 <span class="comment">// </span>
<a name="l00008"></a>00008 <span class="comment">//   * Redistributions of source code must retain the above copyright</span>
<a name="l00009"></a>00009 <span class="comment">//     notice, this list of conditions and the following disclaimer.</span>
<a name="l00010"></a>00010 <span class="comment">//   * Redistributions in binary form must reproduce the above</span>
<a name="l00011"></a>00011 <span class="comment">//     copyright notice, this list of conditions and the following disclaimer</span>
<a name="l00012"></a>00012 <span class="comment">//     in the documentation and/or other materials provided with the</span>
<a name="l00013"></a>00013 <span class="comment">//     distribution.</span>
<a name="l00014"></a>00014 <span class="comment">//   * Neither the name of Google Inc. nor the names of its</span>
<a name="l00015"></a>00015 <span class="comment">//     contributors may be used to endorse or promote products derived from</span>
<a name="l00016"></a>00016 <span class="comment">//     this software without specific prior written permission.</span>
<a name="l00017"></a>00017 <span class="comment">//</span>
<a name="l00018"></a>00018 <span class="comment">// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS</span>
<a name="l00019"></a>00019 <span class="comment">// &quot;AS IS&quot; AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT</span>
<a name="l00020"></a>00020 <span class="comment">// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR</span>
<a name="l00021"></a>00021 <span class="comment">// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT</span>
<a name="l00022"></a>00022 <span class="comment">// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,</span>
<a name="l00023"></a>00023 <span class="comment">// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT</span>
<a name="l00024"></a>00024 <span class="comment">// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,           </span>
<a name="l00025"></a>00025 <span class="comment">// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY           </span>
<a name="l00026"></a>00026 <span class="comment">// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT</span>
<a name="l00027"></a>00027 <span class="comment">// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE</span>
<a name="l00028"></a>00028 <span class="comment">// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.</span>
<a name="l00029"></a>00029 <span class="comment">// -----------------------------------------------------------------------------</span>
<a name="l00030"></a>00030 <span class="comment">//</span>
<a name="l00031"></a>00031 <span class="comment">//</span>
<a name="l00036"></a>00036 <span class="comment"></span>
<a name="l00037"></a>00037 <span class="preprocessor">#include &lt;cmath&gt;</span>
<a name="l00038"></a>00038 <span class="preprocessor">#include &lt;cstdio&gt;</span>
<a name="l00039"></a>00039 <span class="preprocessor">#include &lt;iostream&gt;</span>
<a name="l00040"></a>00040 <span class="preprocessor">#include &lt;stdlib.h&gt;</span>
<a name="l00041"></a>00041 <span class="preprocessor">#include &quot;../proto/model.pb.h&quot;</span>
<a name="l00042"></a>00042 <span class="preprocessor">#include &quot;<a class="code" href="training-vector-set_8_h.html" title="Provides the reranker::TrainingVectorSet class.">training-vector-set.H</a>&quot;</span>
<a name="l00043"></a>00043 <span class="preprocessor">#include &quot;<a class="code" href="perceptron-model-proto-reader_8_h.html" title="De-serializer for reranker::PerceptronModel instances from ModelMessage instances.">perceptron-model-proto-reader.H</a>&quot;</span>
<a name="l00044"></a>00044 
<a name="l00045"></a>00045 <span class="keyword">namespace </span>reranker {
<a name="l00046"></a>00046 
<a name="l00047"></a><a class="code" href="namespacereranker.html#ad611f8447b939da0b87cff159dfbdee6">00047</a> <a class="code" href="model-proto-reader_8_h.html#a6592f002ac5117ad9820aa19b8e4a1fa" title="Registers the ModelProtoReader  implementation with the specified subtype TYPE with the ModelProtoRea...">REGISTER_MODEL_PROTO_READER</a>(<a class="code" href="classreranker_1_1_perceptron_model_proto_reader.html" title="A class to construct a PerceptronModel from a ModelMessage instance.">PerceptronModelProtoReader</a>)
<a name="l00048"></a>00048 
<a name="l00049"></a>00049 using confusion_learning::FeatureMessage;
<a name="l00050"></a>00050 using confusion_learning::SymbolTableMessage;
<a name="l00051"></a>00051 using confusion_learning::SymbolMessage;
<a name="l00052"></a>00052 
<a name="l00053"></a>00053 <span class="keywordtype">void</span>
<a name="l00054"></a><a class="code" href="classreranker_1_1_perceptron_model_proto_reader.html#a15d0fdf95b8c589c3451b813b3f25d54">00054</a> <a class="code" href="classreranker_1_1_perceptron_model_proto_reader.html" title="A class to construct a PerceptronModel from a ModelMessage instance.">PerceptronModelProtoReader</a>::Read(const ModelMessage &amp;model_message,
<a name="l00055"></a>00055                                  <a class="code" href="classreranker_1_1_model.html" title="Model is an interface for reranking models.">Model</a> *model)<span class="keyword"> const </span>{
<a name="l00056"></a>00056   <a class="code" href="classreranker_1_1_perceptron_model.html" title="This class implements a perceptron model reranker.">PerceptronModel</a> *perceptron_model = <span class="keyword">static_cast&lt;</span><a class="code" href="classreranker_1_1_perceptron_model.html" title="This class implements a perceptron model reranker.">PerceptronModel</a> *<span class="keyword">&gt;</span>(model);
<a name="l00057"></a>00057   perceptron_model-&gt;<a class="code" href="classreranker_1_1_model.html#ac6e00f894c28c3bc36636983a0fc2bb6" title="This model&rsquo;s unique name.">name_</a> = model_message.identifier();
<a name="l00058"></a>00058   perceptron_model-&gt;<a class="code" href="classreranker_1_1_perceptron_model.html#acc08b37db0b4b7ea4cd8d340a4e86216" title="The epoch of the best models seen so far during training.">best_model_epoch_</a> = model_message.num_iterations();
<a name="l00059"></a>00059   perceptron_model-&gt;<a class="code" href="classreranker_1_1_model.html#ac23eff7aa52c83c566658bcba9093c4f" title="The tiny object that holds the &quot;training time&quot; for this model (epoch, index and absolute time index)...">time_</a> = <a class="code" href="classreranker_1_1_time.html" title="A simple class to hold the three notions of time during training: the current epoch, the current time index within the current epoch, and the absolute time index.">Time</a>(perceptron_model-&gt;<a class="code" href="classreranker_1_1_perceptron_model.html#acc08b37db0b4b7ea4cd8d340a4e86216" title="The epoch of the best models seen so far during training.">best_model_epoch_</a>, -1, -1);
<a name="l00060"></a>00060   <span class="comment">// TODO(dbikel): Emit warning if model_message.has_symbols() returns true</span>
<a name="l00061"></a>00061   <span class="comment">//               when perceptron_model-&gt;symbols_ is NULL?</span>
<a name="l00062"></a>00062   <span class="keywordflow">if</span> (perceptron_model-&gt;<a class="code" href="classreranker_1_1_model.html#a8c0f904234605692035ba179f85c53e2" title="The symbol table for this model (may be NULL).">symbols_</a> != NULL &amp;&amp; model_message.has_symbols()) {
<a name="l00063"></a>00063     <span class="keyword">const</span> SymbolTableMessage &amp;symbol_table_message = model_message.symbols();
<a name="l00064"></a>00064     <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; symbol_table_message.symbol_size(); ++i) {
<a name="l00065"></a>00065       <span class="keyword">const</span> SymbolMessage &amp;symbol_message = symbol_table_message.symbol(i);
<a name="l00066"></a>00066       perceptron_model-&gt;<a class="code" href="classreranker_1_1_model.html#a8c0f904234605692035ba179f85c53e2" title="The symbol table for this model (may be NULL).">symbols_</a>-&gt;<a class="code" href="classreranker_1_1_symbols.html#aae3fb0d876b17204049c3de8ae8b30f9">SetIndex</a>(symbol_message.symbol(),
<a name="l00067"></a>00067                                            symbol_message.index());
<a name="l00068"></a>00068     }
<a name="l00069"></a>00069   }
<a name="l00070"></a>00070   <span class="comment">// TODO(dbikel): De-serialize model loss.</span>
<a name="l00071"></a>00071   <span class="keywordflow">if</span> (model_message.has_raw_parameters()) {
<a name="l00072"></a>00072     fv_reader_.Read(model_message.raw_parameters(),
<a name="l00073"></a>00073                     perceptron_model-&gt;<a class="code" href="classreranker_1_1_perceptron_model.html#a234283287d5a7f59fd8a1d1cf767fbc1" title="The best models seen so far during training, according to evaluation on the held-out development test...">best_models_</a>.weights_,
<a name="l00074"></a>00074                     perceptron_model-&gt;<a class="code" href="classreranker_1_1_model.html#a736bec0b8f468285580982df181a7cd0" title="Returns the symbol table for this model.">symbols</a>());
<a name="l00075"></a>00075   }
<a name="l00076"></a>00076   <span class="keywordflow">if</span> (model_message.has_avg_parameters()) {
<a name="l00077"></a>00077     fv_reader_.Read(model_message.avg_parameters(),
<a name="l00078"></a>00078                     perceptron_model-&gt;<a class="code" href="classreranker_1_1_perceptron_model.html#a234283287d5a7f59fd8a1d1cf767fbc1" title="The best models seen so far during training, according to evaluation on the held-out development test...">best_models_</a>.average_weights_,
<a name="l00079"></a>00079                     perceptron_model-&gt;<a class="code" href="classreranker_1_1_model.html#a736bec0b8f468285580982df181a7cd0" title="Returns the symbol table for this model.">symbols</a>());
<a name="l00080"></a>00080   }
<a name="l00081"></a>00081   <span class="comment">// Do &quot;smart copying&quot;.</span>
<a name="l00082"></a>00082   <span class="keywordflow">if</span> (smart_copy_) {
<a name="l00083"></a>00083     <span class="keywordflow">if</span> (perceptron_model-&gt;<a class="code" href="classreranker_1_1_perceptron_model.html#a234283287d5a7f59fd8a1d1cf767fbc1" title="The best models seen so far during training, according to evaluation on the held-out development test...">best_models_</a>.weights_.<a class="code" href="classreranker_1_1_feature_vector.html#a08a5b65bae6cc0bb35e035e5797a00f2" title="Returns the number of non-zero feature components of this feature vector.">size</a>() == 0 &amp;&amp;
<a name="l00084"></a>00084         perceptron_model-&gt;<a class="code" href="classreranker_1_1_perceptron_model.html#a234283287d5a7f59fd8a1d1cf767fbc1" title="The best models seen so far during training, according to evaluation on the held-out development test...">best_models_</a>.average_weights_.<a class="code" href="classreranker_1_1_feature_vector.html#a08a5b65bae6cc0bb35e035e5797a00f2" title="Returns the number of non-zero feature components of this feature vector.">size</a>() &gt; 0) {
<a name="l00085"></a>00085       perceptron_model-&gt;<a class="code" href="classreranker_1_1_perceptron_model.html#a234283287d5a7f59fd8a1d1cf767fbc1" title="The best models seen so far during training, according to evaluation on the held-out development test...">best_models_</a>.weights_ =
<a name="l00086"></a>00086           perceptron_model-&gt;<a class="code" href="classreranker_1_1_perceptron_model.html#a234283287d5a7f59fd8a1d1cf767fbc1" title="The best models seen so far during training, according to evaluation on the held-out development test...">best_models_</a>.average_weights_;
<a name="l00087"></a>00087     } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (perceptron_model-&gt;<a class="code" href="classreranker_1_1_perceptron_model.html#a234283287d5a7f59fd8a1d1cf767fbc1" title="The best models seen so far during training, according to evaluation on the held-out development test...">best_models_</a>.average_weights_.<a class="code" href="classreranker_1_1_feature_vector.html#a08a5b65bae6cc0bb35e035e5797a00f2" title="Returns the number of non-zero feature components of this feature vector.">size</a>() == 0 &amp;&amp;
<a name="l00088"></a>00088                perceptron_model-&gt;<a class="code" href="classreranker_1_1_perceptron_model.html#a234283287d5a7f59fd8a1d1cf767fbc1" title="The best models seen so far during training, according to evaluation on the held-out development test...">best_models_</a>.weights_.<a class="code" href="classreranker_1_1_feature_vector.html#a08a5b65bae6cc0bb35e035e5797a00f2" title="Returns the number of non-zero feature components of this feature vector.">size</a>() &gt; 0) {
<a name="l00089"></a>00089       perceptron_model-&gt;<a class="code" href="classreranker_1_1_perceptron_model.html#a234283287d5a7f59fd8a1d1cf767fbc1" title="The best models seen so far during training, according to evaluation on the held-out development test...">best_models_</a>.average_weights_ = 
<a name="l00090"></a>00090           perceptron_model-&gt;<a class="code" href="classreranker_1_1_perceptron_model.html#a234283287d5a7f59fd8a1d1cf767fbc1" title="The best models seen so far during training, according to evaluation on the held-out development test...">best_models_</a>.weights_;
<a name="l00091"></a>00091     }
<a name="l00092"></a>00092   }
<a name="l00093"></a>00093 
<a name="l00094"></a>00094   <span class="comment">// Finally, make sure best_models_ is copied to models_.</span>
<a name="l00095"></a>00095   perceptron_model-&gt;<a class="code" href="classreranker_1_1_perceptron_model.html#a6f913688d02fe624d91d1416c71e157c" title="The feature vectors representing this model.">models_</a> = perceptron_model-&gt;<a class="code" href="classreranker_1_1_perceptron_model.html#a234283287d5a7f59fd8a1d1cf767fbc1" title="The best models seen so far during training, according to evaluation on the held-out development test...">best_models_</a>;
<a name="l00096"></a>00096 }
<a name="l00097"></a>00097 
<a name="l00098"></a><a class="code" href="classreranker_1_1_perceptron_model_proto_reader.html#a6ae54905e41bc2e509939bec4e6e0133">00098</a> <span class="keywordtype">void</span> <a class="code" href="classreranker_1_1_perceptron_model_proto_reader.html#a6ae54905e41bc2e509939bec4e6e0133" title="De-serializes Features from an instance.">PerceptronModelProtoReader::ReadFeatures</a>(istream&amp; is,
<a name="l00099"></a>00099                                               <a class="code" href="classreranker_1_1_model.html" title="Model is an interface for reranking models.">Model</a> *model,
<a name="l00100"></a>00100                                               <span class="keywordtype">bool</span> skip_key,
<a name="l00101"></a>00101                                               <span class="keyword">const</span> <span class="keywordtype">string</span>&amp; separator)<span class="keyword"> const </span>{
<a name="l00102"></a>00102   <a class="code" href="classreranker_1_1_perceptron_model.html" title="This class implements a perceptron model reranker.">PerceptronModel</a> *perceptron_model = <span class="keyword">dynamic_cast&lt;</span><a class="code" href="classreranker_1_1_perceptron_model.html" title="This class implements a perceptron model reranker.">PerceptronModel</a> *<span class="keyword">&gt;</span>(model);
<a name="l00103"></a>00103   <a class="code" href="classreranker_1_1_training_vector_set.html" title="A class to hold the several feature vectors needed during training (especially for the perceptron fam...">TrainingVectorSet</a> &amp;features = perceptron_model-&gt;<a class="code" href="classreranker_1_1_perceptron_model.html#a234283287d5a7f59fd8a1d1cf767fbc1" title="The best models seen so far during training, according to evaluation on the held-out development test...">best_models_</a>;
<a name="l00104"></a>00104   <a class="code" href="classreranker_1_1_symbols.html" title="An interface specifying a converter from symbols (strings) to int indices.">Symbols</a> *symbols = perceptron_model-&gt;<a class="code" href="classreranker_1_1_model.html#a736bec0b8f468285580982df181a7cd0" title="Returns the symbol table for this model.">symbols</a>();
<a name="l00105"></a>00105   ConfusionProtoIO proto_reader;
<a name="l00106"></a>00106   <span class="keywordtype">string</span> buffer;
<a name="l00107"></a>00107   <span class="keywordflow">while</span> (is &amp;&amp; is.good()) {
<a name="l00108"></a>00108     getline(is, buffer);
<a name="l00109"></a>00109     <span class="keywordflow">if</span> (buffer.empty()) {
<a name="l00110"></a>00110       <span class="keywordflow">break</span>;
<a name="l00111"></a>00111     }
<a name="l00112"></a>00112     <span class="keywordflow">if</span> (skip_key) {
<a name="l00113"></a>00113       <span class="keywordtype">size_t</span> seppos = buffer.find(separator);
<a name="l00114"></a>00114       <span class="keywordflow">if</span> (seppos != string::npos) {
<a name="l00115"></a>00115         buffer.erase(0, seppos+1);
<a name="l00116"></a>00116       }
<a name="l00117"></a>00117     }
<a name="l00118"></a>00118     FeatureMessage feature_msg;
<a name="l00119"></a>00119     <span class="keywordflow">if</span> (!proto_reader.DecodeBase64(buffer, &amp;feature_msg)) {
<a name="l00120"></a>00120       cerr &lt;&lt; <span class="stringliteral">&quot;Error decoding: &quot;</span> &lt;&lt; feature_msg.Utf8DebugString() &lt;&lt; endl;
<a name="l00121"></a>00121       <span class="keywordflow">continue</span>;
<a name="l00122"></a>00122     }
<a name="l00123"></a>00123     <span class="keywordtype">int</span> uid = feature_msg.id();
<a name="l00124"></a>00124     <span class="keywordflow">if</span> (symbols != NULL &amp;&amp;
<a name="l00125"></a>00125         feature_msg.has_name() &amp;&amp; !feature_msg.name().empty()) {
<a name="l00126"></a>00126       uid = symbols-&gt;<a class="code" href="classreranker_1_1_symbols.html#afbf1303d9200f2a0880d1330a89186c1" title="Converts the specified symbol to a unique integer.">GetIndex</a>(feature_msg.name());
<a name="l00127"></a>00127     }
<a name="l00128"></a>00128     <span class="keywordtype">double</span> value = feature_msg.value();
<a name="l00129"></a>00129     <span class="keywordflow">if</span> (isnan(value)) {
<a name="l00130"></a>00130         cerr &lt;&lt; <span class="stringliteral">&quot;PerceptronModelProtoReader: WARNING: feature &quot;</span>
<a name="l00131"></a>00131              &lt;&lt; uid &lt;&lt; <span class="stringliteral">&quot; has value that is NaN&quot;</span> &lt;&lt; endl;
<a name="l00132"></a>00132     } <span class="keywordflow">else</span> {
<a name="l00133"></a>00133       features.weights_.<a class="code" href="classreranker_1_1_feature_vector.html#a541f26184a4407f62eb5dad8102cf375" title="Increments the weight of the specified feature by the specified amount.">IncrementWeight</a>(uid, value);
<a name="l00134"></a>00134     }
<a name="l00135"></a>00135     <span class="keywordflow">if</span> (feature_msg.has_avg_value()) {
<a name="l00136"></a>00136       <span class="keywordtype">double</span> avg_value = feature_msg.avg_value();
<a name="l00137"></a>00137       <span class="keywordflow">if</span> (isnan(avg_value)) {
<a name="l00138"></a>00138         cerr &lt;&lt; <span class="stringliteral">&quot;PerceptronModelProtoReader: WARNING: feature &quot;</span>
<a name="l00139"></a>00139              &lt;&lt; uid &lt;&lt; <span class="stringliteral">&quot; has avg_value that is NaN&quot;</span> &lt;&lt; endl;
<a name="l00140"></a>00140       } <span class="keywordflow">else</span> {
<a name="l00141"></a>00141         features.average_weights_.<a class="code" href="classreranker_1_1_feature_vector.html#a541f26184a4407f62eb5dad8102cf375" title="Increments the weight of the specified feature by the specified amount.">IncrementWeight</a>(uid, avg_value);
<a name="l00142"></a>00142       }
<a name="l00143"></a>00143     }
<a name="l00144"></a>00144   }
<a name="l00145"></a>00145   <span class="comment">// Do &quot;smart copying&quot;.</span>
<a name="l00146"></a>00146   <span class="keywordflow">if</span> (smart_copy_) {
<a name="l00147"></a>00147     <span class="keywordflow">if</span> (features.weights_.<a class="code" href="classreranker_1_1_feature_vector.html#a08a5b65bae6cc0bb35e035e5797a00f2" title="Returns the number of non-zero feature components of this feature vector.">size</a>() == 0 &amp;&amp; features.average_weights_.<a class="code" href="classreranker_1_1_feature_vector.html#a08a5b65bae6cc0bb35e035e5797a00f2" title="Returns the number of non-zero feature components of this feature vector.">size</a>() &gt; 0) {
<a name="l00148"></a>00148       features.weights_ = features.average_weights_;
<a name="l00149"></a>00149     } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (features.average_weights_.<a class="code" href="classreranker_1_1_feature_vector.html#a08a5b65bae6cc0bb35e035e5797a00f2" title="Returns the number of non-zero feature components of this feature vector.">size</a>() == 0 &amp;&amp;
<a name="l00150"></a>00150                features.weights_.<a class="code" href="classreranker_1_1_feature_vector.html#a08a5b65bae6cc0bb35e035e5797a00f2" title="Returns the number of non-zero feature components of this feature vector.">size</a>() &gt; 0) {
<a name="l00151"></a>00151       features.average_weights_ = features.weights_;
<a name="l00152"></a>00152     }
<a name="l00153"></a>00153   }
<a name="l00154"></a>00154   <span class="comment">// Make sure to copy latest model to models_.</span>
<a name="l00155"></a>00155   perceptron_model-&gt;<a class="code" href="classreranker_1_1_perceptron_model.html#a6f913688d02fe624d91d1416c71e157c" title="The feature vectors representing this model.">models_</a> = features;
<a name="l00156"></a>00156 }
<a name="l00157"></a>00157 
<a name="l00158"></a>00158 }  <span class="comment">// namespace reranker</span>
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